MI300X vs RTX 5070 Ti

The MI300X achieves 1307.4 TFLOPS in dense FP16 while the RTX 5070 Ti reaches 61.7 TFLOPS in dense FP16. Inference workloads benefit similarly when using dense FP16 operations on the MI300X compared to the RTX 5070 Ti. Memory bandwidth of 5300 GB/s in the MI300X family versus 672 GB/s in the RTX 5070 family permits larger batch sizes during both training and inference on the MI300X. The FP32 performance stands at 163.4 TFLOPS for the MI300X family and 30.9 TFLOPS for the RTX 5070 family. When sparsity is applied the MI300X reaches 2614.9 TFLOPS in FP16 with sparsity against 123.5 TFLOPS in FP16 with sparsity on the RTX 5070 Ti. The MI300X also provides 2614.9 TFLOPS in dense FP8 compared to 123.5 TFLOPS in dense FP8 on the RTX 5070 Ti.

The MI300X with 192 GB HBM3 suits scenarios that demand large model training where memory capacity exceeds 16 GB. Its 750W TDP supports sustained high throughput in data center environments that require the 5300 GB/s bandwidth for efficient data movement. Workloads needing 1307.4 TFLOPS dense FP16 benefit directly from this variant.

The RTX 5070 Ti with 16 GB GDDR7 fits consumer grade setups that operate within 250W power limits. Tasks that do not require more than 16 GB of memory run effectively on this PCIe form factor GPU with 61.7 TFLOPS dense FP16 available.